> I'm sure it was very difficult to program in machine code, but if now (or soon) anyone can just write software using a LLM without any sort of learning it changes everything. LLMs can plan and create something usable from simple instructions or ideas, and they will only get better.

Did you read the section "Power to the People?" ? In it, the author dismantles your thesis with powerful, highly plausible arguments.

I read that section but I disagree with it.

1. You don't have to be an LLM expert to get good, consistent results with LLMs.

My best vibe-code process after years of using LLMs is to have Claude Code create a plan file and then cycle it through Codex until Codex finds nothing more to review, then have an agent implement it. This process is trivial yet produces amazing results.

It's solved by better and better harnesses.

2. You don't have to write technical specs. The LLM does that for you. You just tell it "I want the next-tab button to wrap back to the first one" and it generates a technical plan. Natural language is fine.

3. Software that seems to work only to fail down the line in production is already how software works today. With LLMs you can paste the stacktrace or user bug email and it will fix it.

This is why vibe-coding works. Instead of simulating how an app will run in your head looking at its code, you run the app and tell the LLM what isn't working correctly. The app spec is derived iteratively through a UX feedback look.

4. I don't understand TFA's goalposts, but letting people create software that are only interested in the LLM process (rather than the software craftsmanship) would be a huge democratization of software.

While I think the author is entirely right about 'natural language programming' in the current day, if LLMs (or some other AI architecture) continue to improve, it is easy to believe touching code could become unnecessary for even large projects. Consider that this is what software co. executives do all the time: outline a high level goal (software product) to their engineering director, who largely handles the details. We just don't yet know if LLMs will ever manage a level of intelligence and independence in open-ended tasks like this. And, to expand on that, I don't know that intelligence is necessarily the bottleneck for this goal. They can clearly tackle even large engineering tasks, but often complaints are that they miss on important architectural context or choose a suboptimal solution. Maybe with better training, context handling, documentation, these things will cease to be problems.

I have indeed missed the arguments that are so powerful that they dismantles my thesis.

Would there even be a debate in the tech community if such unassailable arguments existed? The author is entirely entitled to his opinion, just as I am allowed to disagree with him (not sure why I am also downvoted). The good thing is, if I'm right, we will see it in less than 10 years.